{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_fill_identity`\n",
    "`scale_fill_identity` applies the fill values in your data to your ggplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>ze-color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>MediumAquaMarine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Peru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Tomato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>#f8b195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>#ffb6c1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>#933835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Bisque</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y          ze-color\n",
       "0  0  0              blue\n",
       "1  1  1               red\n",
       "2  2  2             green\n",
       "3  3  3  MediumAquaMarine\n",
       "4  4  4              Peru\n",
       "5  5  5            Tomato\n",
       "6  6  6           #f8b195\n",
       "7  7  7           #ffb6c1\n",
       "8  8  8           #933835\n",
       "9  9  9            Bisque"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ze_colors = [\n",
    "    \"blue\",\n",
    "    \"red\",\n",
    "    \"green\",\n",
    "    \"MediumAquaMarine\",\n",
    "    \"Peru\",\n",
    "    \"Tomato\",\n",
    "    \"#f8b195\",\n",
    "    \"#ffb6c1\",\n",
    "    \"#933835\",\n",
    "    \"Bisque\"\n",
    "]\n",
    "df = pd.DataFrame({\n",
    "        \"x\": range(10),\n",
    "        \"y\": range(10),\n",
    "        \"ze-color\": ze_colors\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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f3Qg9ffp0TZgwQTNnzlRSUpLS09O1d+/e4DbZSBEaAAAAYAkvvPBCjWU333xzret36tRJ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\nNIRt27ZpxIgR6tu3r3bs2OF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c264d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (274640201)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df, aes(x='x', y='y', fill='ze-color')) + \\\n",
    "    geom_bar() + \\\n",
    "    scale_fill_identity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
